To link to the entire object, paste this link in email, IM or documentTo embed the entire object, paste this HTML in websiteTo link to this page, paste this link in email, IM or documentTo embed this page, paste this HTML in website

STATISTICAL LESION DETECTION
IN DYNAMIC POSITRON EMISSION TOMOGRAPHY
by
Zheng Li
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
May 2008
Copyright 2008 Zheng Li

Positron emission tomography (PET) with F-18 fluorodeoxyglucose (FDG) is improving the diagnosis, staging and treatment monitoring of a variety of cancers in comparison to alternative noninvasive imaging modalities. However, detection of small lesions is limited by image resolution and low signal to noise ratio. While computer-aided detection algorithms based on static PET may assist visual detection of lesions, the temporal domain information loss in the static image could impair the detection of lesions. In addition, spatial features such as the shape and contrast of a lesion in static PET images are highly variable and difficult to characterize. Since the dynamics of tracer uptake (tracer radioactivity) are different for normal and malignant tissue, the temporal domain provides potentially important additional features for use in lesion detection.; This dissertation focuses on three challenges in the detection of lesions in dynamic PET: feature extraction, detector design and thresholding procedure. A linear subspace model with additive Gaussian noise is used to describe the received time activity curve (TAC) which is a function of local tracer radioactivity versus time. Using TACs from a primary tumor region of interest (ROI) and one or multiple normal ROIs, two linear subspaces can be identified. We use two sets of subspaces instead of shape or intensity information as the features in lesion detection. A matched subspace detection algorithm based on the generalized likelihood ratio test [VO91, Sch90] was proposed to assist in the detection of small tumors. The algorithm differentiates tumor tissue from nontumor background using the TACs that characterize the uptake of PET tracers. Applying a matched subspace detector with the identified subspaces to TACs on a voxel by voxel basis throughout the dynamic image produces a test statistic at each voxel which onthresholding indicates potential locations of secondary or metastatic tumors. Applying the detector to the dynamic image results in a statistic image, or a statistic map. The detector is derived for three cases: detection on a single TAC with unknown variance white noise, detection on a single TAC with known noise covariance, and detection on multiple TACs within a small ROI with known noise covariance.; To make the binary decision on the presence or absence of a lesion at each voxel, the continuous-value statistic map is processed by a constant familywise error rate (FWER) thresholding algorithm. The statistic map is modeled as an inhomogeneous Gaussian random field. The thresholding procedure includes three steps. First, the PET image is segmented into several homogeneous regions. Then the statistic map is normalized to a zero mean unit variance Gaussian random field. Finally a maximum statistic thresholding with a fixed FWER is applied.; To evaluate the proposed matched subspace detector, a receiver operation characteristic (ROC) study for dynamic PET tumor detection was designed. The detector uses a dynamic sequence of frame-by-frame 2-dimensional (2-D) MAP reconstructions as input. We compared the performance of subspace detectors with that of a Hotelling observer [BGG+92] applied to a single frame image and parametric Patlak [PBF83] method. Our Monte Carlo simulations show that the matched subspace detector is superior to the 2-D static Hotelling observer and the Patlak method for the lesion detection task. The application of detection approaches to clinical PET data was also demonstrated. To evaluate the proposed thresholding algorithm, we used a digital phantom generated from clinical dynamic images. Simulations show that with a preset FWER=5%, the proposed thresholding method achieves a 97.1% true positive rate, with 1.8% FWER. We also demonstrate here the application of the proposed approach to clinical PET datafrom a breast cancer patient with metastatic disease.

STATISTICAL LESION DETECTION
IN DYNAMIC POSITRON EMISSION TOMOGRAPHY
by
Zheng Li
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
May 2008
Copyright 2008 Zheng Li